Digital systems in pharmacovigilance: Enhancing adverse drug reaction detection in endocrine disorders

Objective: To examine the efficacy of digital systems in improving the detection and management of adverse drug reactions (ADRs) in patients with endocrine disorders, focusing on diabetes, hypothyroidism, and hyperprolactinemia. Methods: A retrospective observational study analyzed data from electronic health records (EHRs), the National Pharmacovigilance Center, and the global VigiBase database (2019-2023). Quantitative analysis compared ADR reporting rates via traditional and digital channels using chi-square tests and ROC analysis to evaluate AI model accuracy. Qualitative analysis included a survey of 120 healthcare workers and thematic coding of responses. Results: Digital systems increased ADR reporting by 335% compared to traditional methods (p < 0.01). AI models (Random Forest, XGBoost) achieved 89% sensitivity, 92% specificity, and an AUC of 0.94, identifying 17% more high-risk drug interactions in diabetic patients. The survey revealed 78% of respondents found digital tools more efficient, but 30% highlighted the need for additional training. Conclusion: Digital systems significantly enhance pharmacovigilance for endocrine disorders through faster ADR detection, precise risk analysis, and greater healthcare worker engagement. Key recommendations include investing in interoperable EHR platforms, staff training, and standardized protocols. This study emphasizes the necessity of integrating innovative technologies into clinical practice to improve therapy safety.